System Observability Analysis Based on Multiple Source Mixed Traffic Sensors

Guo, Yuqi; Li, Honghai; Li, Xiyao; Gu, Lisha; Zhang, Jinjin

doi:10.1109/cac51589.2020.9326826

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…one of the first and key jobs is to establish mixed traffic flow model. The highway network will be as the example to study the observability of mixed traffic flow system, based on modeling methods and results proposed in literature [4][5][6][7][8][9][15][16][17], the mixed traffic flow model is described in the following.…”

Section: Mixed Traffic Flow Modelmentioning

confidence: 99%

“…The observability algorithm on highway segments was presented to placed traffic sensors efficiently to maximize the information collected [3]. Based on the traffic flow model, the optimized placement of traffic sensor network and the observability analysis were also studied [4][5][6][7][8][9]. Many other similar approaches were also applied [10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

System observability based traffic security risk sensors optimized layout analysis under the intelligent connected scenario

Guo

Xing

Li-li

et al. 2022

Seventh International Conference on Electromechanical Control Technology and Transportation (ICECTT 2022)

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Section: Mixed Traffic Flow Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

System observability based traffic security risk sensors optimized layout analysis under the intelligent connected scenario

Guo

Xing

Li-li

et al. 2022

Seventh International Conference on Electromechanical Control Technology and Transportation (ICECTT 2022)

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Designing and Implementing a Public Urban Transport Scheduling System Based on Artificial Intelligence for Smart Cities

Rosca,

Stancu,

Neculaiu

et al. 2024

Applied Sciences

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Many countries encourage their populations to use public urban transport to decrease pollution and traffic congestion. However, this can generate overcrowded routes at certain times and low economic efficiency for public urban transport companies when buses carry few passengers. This article proposes a Public Urban Transport Scheduling System (PUTSS) algorithm for allocating a public urban transport fleet based on the number of passengers waiting for a bus and considering the efficiency of public urban transport companies. The PUTSS algorithm integrates artificial intelligence (AI) methods to identify the number of people waiting at each station through real-time image acquisition. The technique presented is Azure Computer Vision. In a case study, the accuracy of correctly identifying the number of persons in an image was computed using the Microsoft Azure Computer Vision service. The proposed PUTSS algorithm also uses Google Maps Service for congestion-level identification. Employing these modern tools in the algorithm makes improving public urban transport services possible. The algorithm is integrated into a software application developed in C#, simulating a real-world scenario involving two public urban transport vehicles. The global accuracy rate of 89.81% demonstrates the practical applicability of the software product.

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System Observability Analysis Based on Multiple Source Mixed Traffic Sensors

Cited by 2 publications

References 14 publications

System observability based traffic security risk sensors optimized layout analysis under the intelligent connected scenario

System observability based traffic security risk sensors optimized layout analysis under the intelligent connected scenario

Designing and Implementing a Public Urban Transport Scheduling System Based on Artificial Intelligence for Smart Cities

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